Patent Pools and Cross-Licensing in the Shadow of Patent Litigation

This paper develops a framework to analyze the incentives to form a patent pool or engage in cross-licensing arrangements in the presence of uncertainty about the validity and coverage of patents that makes disputes inevitable. It analyzes the private incentives to litigate and compares them with the social incentives. It shows that pooling arrangements can have the effect of sheltering invalid patents from challenges. This result has an antitrust implication that patent pools should not be permitted until after patentees have challenged the validity of each other’s patents if litigation costs are not too large.

Pools and Cross-Licensing in the Shadow of Patent Litigation

Most patent pools are formed in the shadow of patent litigation as an attempt to settle disputes in regard to conflicting infringement claims and the validity of patents. To reflect this reality, I develop a simple framework to analyze the incentives to form a patent pool or engage in cross-licensing arrangements in the presence of uncertainty as to the validity and coverage of patents that makes disputes inevitable. I analyze private incentives to litigate and compare them with the social incentives. Antitrust implications of patent pools are considered. The effects of patent pools on third party incentives to challenge the validity of patents and on development incentives are also investigated.patent pools, cross-licensing, complements and substitutes, patent litigation

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

<p>Abstract</p> <p>Background</p> <p>Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand.</p> <p>Results</p> <p>In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort.</p> <p>Conclusions</p> <p>The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.</p

Towards Efficient Maximum Likelihood Estimation of LPV-SS Models

How to efficiently identify multiple-input multiple-output (MIMO) linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling variable still remains an open question, as identification methods proposed in the literature suffer heavily from the curse of dimensionality and/or depend on over-restrictive approximations of the measured signal behaviors. However, obtaining an SS model of the targeted system is crucial for many LPV control synthesis methods, as these synthesis tools are almost exclusively formulated for the aforementioned representation of the system dynamics. Therefore, in this paper, we tackle the problem by combining state-of-the-art LPV input-output (IO) identification methods with an LPV-IO to LPV-SS realization scheme and a maximum likelihood refinement step. The resulting modular LPV-SS identification approach achieves statical efficiency with a relatively low computational load. The method contains the following three steps: 1) estimation of the Markov coefficient sequence of the underlying system using correlation analysis or Bayesian impulse response estimation, then 2) LPV-SS realization of the estimated coefficients by using a basis reduced Ho-Kalman method, and 3) refinement of the LPV-SS model estimate from a maximum-likelihood point of view by a gradient-based or an expectation-maximization optimization methodology. The effectiveness of the full identification scheme is demonstrated by a Monte Carlo study where our proposed method is compared to existing schemes for identifying a MIMO LPV system